The Street-View House Number (SVHN) datasethttp://ufldl.stanford.edu/housenumbers/, 2020/07/10., contains house numbers from Google Street View. The challenge of the SVHN dataset is the structured clutter in the background of images. A Convolutional Neural Network (CNN) with two double-Convolutional layers containing 32 and 64 nodes, respectively, is used. A 20-dim. feature representation on top of this architecture is applied to calculate the affinity loss. Results and comparison to Ganin et al. [1]} are shown in Tab. REF . The affinity loss does indeed improve the performance on the target dataset with only a little amount of samples. The performance of our model trained on the SVHN dataset with 10 MNIST samples reaches an accuracy of around 75% and can be compared with Ganin et al. [1]}. In comparison, the baseline model achieves only an accuracy of 3% (see Fig. REF ) on the same data. If there are only a few MNIST samples available, the neural net trained with SVHN and our affinity loss outperforms the baseline model trained solely on the same amount of MNIST samples.
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